자료유형  

 학위논문

Control Number  

0017162448

International Standard Book Number  

9798384049265

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Tang, Luming.

Publication, Distribution, etc. (Imprint  

[S.l.] : Cornell University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

305 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Hariharan, Bharath.

Dissertation Note  

Thesis (Ph.D.)--Cornell University, 2024.

Summary, Etc.  

요약Computer vision has made significant progress in the past decade, primarily due to the dominant supervised learning paradigm, which involves training large-scale neural networks on extensive datasets for each task. However, scalable data and annotation collection often prove to be intractable. In contrast, humans can adapt to new vision tasks with very little data or few labels.This thesis aims to bridge this gap by presenting a practical solution: pre-training deep neural networks on accessible large-scale internet images, and then employing various techniques to adapt these pre-trained models to diverse downstream tasks with minimal or no additional data. In the pre-training stage, I introduce two meta-learning methods to achieve better pre-trained image representations that generalize to novel classes with minimal extra annotations. In the adaptation stage, I demonstrate multiple techniques for effectively adapting pre-trained models to data-constrained downstream tasks such as recognition, dense prediction, 3D generation, and reference-based image completion.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Computer engineering.

Index Term-Uncontrolled  

Computer vision

Index Term-Uncontrolled  

Generative model

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Representation learning

Added Entry-Corporate Name  

Cornell University Computer Science

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

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Control Number  

joongbu:658445